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SageMaker Python SDK

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EVENT TIMELINE

3

COMPANIES · 1 SOURCE · AWS Machine Learning

Amazon SageMaker AI adds training session APIs to run custom training loops on managed GPUs

Amazon has launched SageMaker training session APIs that let developers write custom training loops (SFT, RL, preference optimization, etc.) while SageMaker handles the GPU compute, distribution, checkpointing, and fault recovery. The feature includes three SDK clients in the SageMaker Python SDK, an OpenAI-compatible sampling endpoint, and a published cookbook of SFT and RL recipes (including examples on GSM8K and LoRA workflows).

7.0

MODELS · 2 SOURCES · AWS Machine Learning · The Verge AI

Meta’s Muse Spark Open (816B MoE) added to Amazon SageMaker JumpStart

Meta’s coding-optimized Muse Spark Open (Mixture-of-Experts, 816 billion parameters with 42B active per token and a 1 million-token context window) is now available in Amazon SageMaker JumpStart as two variants (Muse-Spark-Open-816B-A42B BF16 and Muse-Spark-Open-816B-A42B-NVFP4 NVFP4). The model’s open weights (released under the Muse Model License) can be deployed to SageMaker AI endpoints in your AWS account, with instance recommendations ml.p6-b300.48xlarge for BF16 and ml.p5e.48xlarge for NVFP4 and support for the model’s agentic reasoning and multimodal inputs.

8.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Amazon SageMaker JumpStart adds Model Hub browsing, preconfigured endpoint options, and one‑click Hugging Face deploy (2026)

A new AWS blog post explains how to deploy and benchmark pretrained models using Amazon SageMaker JumpStart and a reusable SageMaker Python SDK v3 script that measures latency, output length, and single-request generation rate. In 2026 SageMaker JumpStart added a Model Hub view in the SageMaker AI console, named preconfigured endpoint options (including Neuron‑optimized and cost/latency profiles), and a deep link to deploy Hugging Face models to SageMaker in one click.

5.0